SPADE (Serendipitous Pareto Distance Evaluation) is a new metric for recommender systems that simultaneously considers item similarity, popularity, and user relevance. It projects items into a two‑dimensional space and computes a user‑specific Pareto frontier of maximally popular and historically similar items, then averages the minimum Euclidean distance from this frontier for correctly recommended test‑set items. Experiments on five datasets and five baseline algorithms demonstrate that SPADE effectively discourages algorithms from exploiting accuracy‑only metrics and reliably isolates serendipitous discoveries.
By Tobias Vente, Maarten Peirsman, Noah Dani\"els, Hannu Toivonen, Bart Goethals
arXiv:2607. 03515v1 Announce Type: cross Abstract: In many machine learning applications, the most relevant items for a query should be efficiently retrieved.
By Kirill Shevkunov, Andrey Ploskonosov, Liudmila Prokhorenkova
arXiv:2606. 07492v1 Announce Type: cross Abstract: The ranking of recommendation algorithms is a challenging problem since model performance is sensitive to dataset characteristics such as sparsity, sequential structure, and scale.
By Ekaterina Grishina, Stepan Kuznetsov, Askar Tsyganov, Ilya Ivanov, Daria Korovaitceva, Margarita Rusanova, Uliana Parkina, Alexander Derevyagin, Evgeny Frolov, Sergey Samsonov, Anton Lysenko
arXiv:2606. 26369v1 Announce Type: cross Abstract: Scoring functions are used to represent the relevance of individual documents.
By Shubham Singh, Ian A. Kash, Mesrob I. Ohannessian
arXiv:2608.21662v1 Announce Type: cross
Abstract: The topic of explanation in recommender systems has seen steady research attention since the earliest days of the field. With some exceptions, this w...
By Meysam Varasteh, Veronika Bogina, Noam Koenigstein, Robin Burke
arXiv:2609.36740v1 Announce Type: new
Abstract: Many recommender systems such as for e-commerce and news platforms aim to provide users with rankings they are likely to interact with. Off-Policy Lear...
By Ren Kishimoto, Koichi Tanaka, Haruka Kiyohara, Yusuke Narita, Yasuo Yamamoto, Nobuyuki Shimizu, Yuta Saito
Re$^3$Rank is a listwise reranker that uses reasoning-conditioned retrieval heads (RRHeads). It generates a reasoning trace for each candidate, then scores candidates by using the trace tokens as attention queries for selected RRHeads, producing continuous relevance scores. A straight‑through estimator allows joint training of the generator and scorer, and on nine benchmarks the 4B model outperforms 11 baseline rerankers by an average of 3.11 NDCG@10 points, while the 0.6B model beats rerankers with up to 32B parameters.
By Ruiyi Yan, Zhuoyuan Mao, Yugo Murawaki, Yiwen Guo
arXiv:2603. 29247v3 Announce Type: replace-cross Abstract: LLM-based shopping agents increasingly rely on long purchase histories and multi-turn interactions for personalization, yet naively appending raw history to prompts is often ineffective due to noise, length, and relevance mismatch.
By Zhiyuan Peng, Xuyang Wu, Huaixiao Tou, Yi Fang, Yu Gong
GUIDE is a large‑language‑model driven architecture that elicits and infers human user preferences through conversational Bayesian adaptive sampling and symbolic rule‑based learning. It extends adaptive sampling to a wide range of elicitation questions via a flexible type system and initializes domain‑specific preference models using symbolic representations of world knowledge. In simulated investment portfolio optimization, GUIDE outperforms prior methods, LLM‑only baselines, and its own ablated variants by improving cold‑start performance and reducing recommendation regret during early interactions.
By Anagha Tiwari, Alexander G. Gray, Nick Feamster, Brian Jabarian, Alex Imas, Alex Kale
arXiv:2608.20801v1 Announce Type: cross
Abstract: While Large Language Models (LLMs) have significantly advanced reranking in recommendation, effectively leveraging item-side information remains chal...
By Dojun Hwang, Seunghan Lee, Cheonyoung Park, Sara Yu, SeongKu Kang
arXiv:2604. 02091v2 Announce Type: replace-cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation.
By Yuhang Wu, Xiangqing Shen, Fanfan Wang, Cangqi Zhou, Zhen Wu, Xinyu Dai, Rui Xia
arXiv:2609.40241v1 Announce Type: cross
Abstract: Large language models (LLMs) have shown promise for recommendation reranking, but their use introduces an important tradeoff between recommendation q...
By Hanjia Lyu, Yinglong Xia